The Earth's energy imbalance is the difference between incoming solar radiation and outgoing reflected and emitted radiation from the Earth, and quantifies the current ongoing accumulation of energy in the Earth's climate system. There are indications that the imbalance is growing, and it is important to be able to measure and monitor this quantity to better constrain future changes. The reflected shortwave component of the outgoing radiation depends on surface and atmospheric properties, which leads to strong directional variations associated with the angular geometry relative to the incoming sunlight and the observer. The effect of this anisotropy on global average measurements from wide-field-of-view radiometers has been the topic of some investigation in the past, and results from an earlier study suggest that this effect could potentially lead to substantial systematic biases in the context of the global mean reflected shortwave radiation. Here we simulate wide-field-of-view instruments on satellites in polar, sun-synchronous and precessing orbits, as well as constellations of these types of satellite orbits, with both Lambertian and anisotropic shortwave reflection. Reference top-of-atmosphere radiative fluxes are taken from the Clouds and the Earth's Radiant Energy System synoptic data product and combined with angular dependence models that were developed for the Earth Radiation Budget Experiment. We find that the polar regions appear brighter and the midlatitudes appear dimmer with anisotropic reflection, but both the estimated global annual mean and the estimated interannual trend only exhibit limited sensitivity to whether Lambertian or anisotropic reflection is assumed. With anisotropic reflection, the estimated global annual mean root-mean-square sampling error is at most 0.11 W m⁻² provided that at least two complementary satellites are used, compared with at most 0.09 W m⁻² in the case of Lambertian reflection. The magnitude of the difference in the estimated interannual trend is at most 0.07 W m⁻² per decade, and typically only ∼ 0.01 W m⁻² per decade. Analysis of the angular sampling of these satellites reveals that the anisotropic reflection requires sufficient sampling of viewing zenith angle and relative azimuth angle, in addition to the solar zenith angle. However, we conclude that it is possible to choose satellite orbits so that the sampling error is not substantially affected by reflected shortwave anisotropy.

Geological evidence of a snowball Earth state indicate persistent tropical sea ice cover during the Neoproterozoic (>635 million years ago). Current theory is that a strengthening of the positive surface albedo feedback with cooling temperatures, eventually exceeding the sum of all other feedbacks, leads to a global climate instability. Several recent high sensitivity climate models with strongly positive cloud feedbacks have not been able to simulate the much warmer Last Glacial Maximum (LGM) state, suggestive that they cool excessively in response to a modest decrease in atmospheric carbon dioxide levels and therefore enter the snowball instability by this mechanism. Using a coupled Earth system model, MPI-ESM1.2, we show that clouds accelerate the transition to a snowball Earth state and reduce the radiative forcing required to trigger the snowball instability. Positive cloud feedbacks over tropical oceans and ahead of the sea-ice edge act to cool down the oceans and promote sea ice formation. Regardless, when approached slowly, the snowball Earth transition appears to occur around a global mean temperature of zero degree Celsius, simultaneously with the sea ice edge advancing into the sub-tropics thereby strengthening the surface albedo feedback. This temperature threshold, if supported by several climate models, could be used as a novel and independent constraint on the upper bound of climate sensitivity by using the relationship of simulated LGM temperatures and the models' equilibrium climate sensitivity. The constraint depends only on the simple fact that Earth did not enter a snowball instability during the recent ice ages. Using the here estimated transition temperature, we find it is implausible that Earth's climate sensitivity exceeds 6.2 °C (3.9–8.4 °C, 5 %–95 % confidence). This upper bound estimate of climate sensitivity is only weakly sensitive to uncertainty in the transition temperature, approximately 0.3° per degree.

Global warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.

The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multidecadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only 3 years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: (1) developing km-scale Earth system models with small errors in the energy and water balance, (2) performing km-scale climate simulations with a throughput greater than 1 simulated year per day, and (3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The hackathon participants included experts from climate science, software engineering, and high-performance computing as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations with ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its time frame, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.

We present the Earth Climate Observatory space mission concept - currently studied in Phase 0 as a European Space Agency Earth Explorer 12 candidate - for the measurement of the Earth Energy Imbalance and the Earth Radiation Budget. Key innovations are 1) the differential Sun-Earth observation with identically constructed wide field of view radiometers, 2) an adequate sampling of the seasonal diurnal cycle with a constellation of polar precessing orbits, 3) complementary full angular coverage at high spatial resolution using wide field of view multispectral cameras.

We present a reconstruction of the surface climate of the mid-Pliocene Warm Period (mPWP), specifically Marine Isotope Stage (MIS) KM5c or 3.205 Ma. We combine the ensemble of climate model simulations, which contributed to the Pliocene Model Intercomparison Project (PlioMIP), with compilations of proxy data analyses of sea surface temperature (SST). The different data sets we considered are all sparse with high uncertainty, and the best estimate of annual global mean surface air temperature (SAT) anomaly varies from 2.1 up to 4.8 °C depending on the data source. We argue that the latest PlioVAR analysis of alkenone data is likely more reliable than other data sets we consider, and using this data set yields an SAT anomaly of 3.9±1.1 °C, with a value of 2.8±0.9 °C for SST (all uncertainties are quoted at 1 standard deviation). However, depending on the application, it may be advisable to consider the broader range arising from the various data sets to account for structural uncertainty. The regional-scale information in the reconstruction may not be reliable as it is largely based on the patterns simulated by the models.

Observing climate feedbacks to long-term global warming, which are crucial climate regulators, is not feasible within the observational record. However, linking them to top-of-the-atmosphere flux variations in response to natural surface temperature fluctuations (internal variability feedbacks) is a viable approach. We explore the use of relating internal variability to forced climate feedbacks in models and applying the resulting relationship to observations to constrain forced climate feedbacks. Our findings reveal strong longwave and shortwave feedback relationships in models during the 14-year overlap with the Clouds and the Earth's Radiant Energy System (CERES) record. Yet, due to the weaker relationship between internal variability and forced climate longwave feedbacks, the net feedback relationship remains weak, even over longer periods beyond the CERES record. However, after about half a century, this relationship strengthens, primarily due to reinforcements of the internal variability and forced climate shortwave feedback relationship. We therefore explore merging the satellite records with reanalysis to establish an extended data record. The resulting constraint suggests a stronger negative forced climate net feedback than the model's distribution and an equilibrium climate sensitivity of about 2.59 K (1.95 to 3.12 K, 5 %–95 % confidence intervals). Nevertheless, this method does not account for certain factors like biogeophysical–chemical feedbacks, inactive on short timescales and not represented in most models, along with differences in historical warming patterns, which may lead to misrepresenting climate sensitivity. Additionally, continuous satellite observations until at least the mid-2030s are essential for using purely observed estimates of the net internal variability feedback to constrain the net forced climate feedback and, consequently, climate sensitivity.

Anthropogenic aerosol particles partially mask global warming driven by greenhouse gases, both directly by reflecting sunlight back to space and indirectly by increasing cloud reflectivity. In recent decades, emissions of anthropogenic aerosols have declined globally and at the same time shifted from the North American and European regions, foremost to Southeast Asia. Using simulations with the Max Planck Institute Earth System Model version 1.2 (MPI-ESM1.2), we find that the direct effect of aerosols has continued to increase despite declining emissions. Concurrently, the indirect effect has diminished in approximate proportion to the emissions. In this model, which employs parameterized aerosol effects with constant regional direct effect efficiency, the enhanced efficiency of aerosol radiative forcing in emissions is associated with less cloud masking, longer atmospheric residence times, and differences in aerosol optical properties.

Recent observation-based studies suggest that the Amazon rainforest has lost substantial resilience since 1990, indicating that the forest might undergo a critical transition in the near future due to global warming and deforestation. The idea is to use trends in a lag-1 auto-correlation of leaf density as an early-warning signal of an imminent critical threshold for rainforest dieback. Here we test whether the observed change in auto-correlations could arise from internal variability using historical and control simulations of nine sixth-generation Earth system model ensembles (Phase 6 of the Coupled Model Intercomparison Project, CMIP6). We quantify trends in the leaf area index auto-correlation from both models and satellite-observed vegetation optical depth from 1990 to 2017. Four models reproduce the observed trend with at least one historical realization whereby the observations lie at the upper limit of model variability. Three out of these four models exhibit similar behavior in control runs, suggesting that historical forcing is not necessary for simulating the observed trends. Furthermore, we do not observe a critical transition in any future runs under the strongest greenhouse gas emission scenario (SSP5-8.5) until 2100 in the four models that best reproduce the past observed trends. Hence, the currently observed trends could be caused simply by internal variability and, unless the data records are extended, have limited applicability as an early-warning signal. Our results suggest that the current rapid decline in the Amazon rainforest coverage is not foremost caused by global warming.

The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO₂ change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO₂ concentrations.